If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Studies forecast that AI will boost profitability by an average of 38% by the year 2035. If an AI program interpreted this data, the result would be conclusive: Artificial intelligence is quickly becoming one of the economy's sharpest competitive edges. According to McKinsey & Company, just 47% of executives report embedding AI into one business process, and only 21% report implementing AI in multiple ways. While the technology is progressing at historically unprecedented rates, the majority of enterprises still face either barriers to entry or difficulty determining next steps. Whether a company is beginning its journey with AI, or ready to take the next measured step, it's essential to conduct a comprehensive AI audit.
New Delhi: In order to make the business operations more efficient and automated, the deployment of software is required in place of manpower. It has been seen in all the businesses where computer machines have taken the place of a number of employees. With the incorporation of software which is set to work on designated protocols, an enterprise can become more cost-efficient and dynamic. Artificial intelligence (AI) and machine learning (ML) are some of the most widely used applications of Information Technology (IT) services which are thoroughly used in large enterprises. The usage of AI and machine learning software in small and medium enterprises (SMEs), small-scale businesses, seasonal businesses and conditional businesses to make the business process more robust is a big question today.
The room was packed at the annual Machine Learning and the Market for Intelligence conference in Toronto last week. Now in its fifth year, the lengthy name of the event matches the depth of the discussions. But one speaker and her talk stood out to me in particular: Marzyeh Ghassemi, who also happens to be a veteran of Alphabet's Verily, presented "Machine Learning From Our Mistakes." Ghassemi, an assistant professor at the University of Toronto, talked about the importance of predicting actionable insights in health care, the regulation of algorithms, and practice data versus knowledge data. But at the very end, saving the best for last, she emphasized the importance of treating health data as a resource.
Artificial intelligence programs are extremely good at finding subtle patterns in enormous amounts of data, but don't understand the meaning of anything. Whether you are searching the Internet on Google, browsing your news feed on Facebook, or finding the quickest route on a traffic app like Waze, an algorithm is at the root of it. Algorithms have permeated our daily lives; they help to simplify, distill, process, and provide insights from massive amounts of data. According to Ernest Davis, a professor of computer science at New York University's Courant Institute of Mathematical Sciences whose research centers on the automation of common-sense reasoning, the technologies that currently exist for artificial intelligence (AI) programs are extremely good at finding subtle patterns in enormous amounts of data. "One way or another," he says, "that is how they work."
The role of a Non-Executive Director (NED) is to represent and safeguard the long-term interests of shareholders and broader stakeholders, including employees, customers, society, and environment. However, continued advances in artificial intelligence, robotic process automation (RPA), and distributed ledger technology like blockchain could bring about a completely new way for the Board to exercise its responsibilities. Ensuring data veracity could be instantaneous and decentralized, allowing for processes such as reviewing company accounts, financial reporting, and auditing to be real-time and immutable, requiring minimal effort on the part of NEDs. Against this backdrop of change, we applied the methodology in our book Reinventing Jobs to reinvent the role of the NED. First we identified all the key activities performed by NEDs.
Vivienne Ming, a theoretical neuroscientist and cofounder of Socos Labs in Berkeley, California, defines artificial intelligence (AI) as "any autonomous and artificial system that can make a decision under uncertainty and make expert human judgements cheaper, faster, and increasingly, in some domains, better than a human can." AI has already been widely applied across business, social, and government sectors. But if it's not applied carefully, AI can lead to distorted results or decisions and potentially exclude historically marginalized or underrepresented populations. On a recent episode of the Urban Institute's podcast, Critical Value, Ming discusses three approaches to minimize the risk of AI supporting problematic or biased outcomes. If AI is trained on biased data and learns from biased samples, the system can reproduce bias that originated from discriminatory human decisions and practices.
Providers continue to fall short of their charge-capture potential despite having rules-based systems and manual audits, an executive at an industry vendor contends. "It's estimated that missing charges and associated reimbursement--combined with audit and recovery efforts--cost providers the equivalent of 1 percent of annual revenue," says Nick Giannasi, executive vice president of Change Healthcare. Industry vendors are designing products to help provider organizations improve their ability to capture charges. For example, Change Healthcare is unveiling a product called Charge Capture Advisor that uses artificial intelligence to identify potentially missing charges for services that providers perform before claims are submitted. The company contends that the result is more complete capture of services rendered without imposing additional time and effort by hospital revenue integrity teams.
Artificial intelligence can help us cope with the growth of work. Whether its Amazon drones putting couriers out of business, AI-powered health checkers diagnosing patients in hospitals, or algorithms at Microsoft providing the perfect recipe for whisky, the growing threat of artificial intelligence (AI) as a global job killer has been a prevailing media story that seems just too good to be false. But the rhetoric is not supported by most recent studies, which suggest that while skill shifts across all industries will certainly be considerable, net job loss over the next 15 years is likely to be negligible. Well, many assumptions imbedded in the "automageddon" narrative are highly questionable: that automation creates few jobs whether short or long term, that whole jobs can be automated, that the technology is perfectible, that organizations can seamlessly and quickly deploy AI, that human thought and action can be replicated, and that it is politically, socially and economically feasible to apply these technologies. Then there are the macro factors.
The risks in the ML life cycle are also different since machine learning models have become pervasive in so many aspects of everyday consumer life – so much of which is tightly regulated. As machine learning models help automate important decisions in a wide variety of industries – banking, health care, airline schedules, telecom, shopping, entertainment, and so on – they become subject to much scrutiny about compliance, audits, needs for explainability, concerns about fairness and bias, privacy laws, security concerns, etc. Many of those activities are regulated, for important reasons. While more traditional software engineering similarly has security concerns, audits, etc., the stakes are not nearly as high: code can be debugged. Machine learning, especially when driven with large scale data, is substantially more difficult to trace and "debug" compared with coding.
There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments.